What is a linear programming model comprised of?

The model of linear programming is fundamentally built on three key components: an objective function, constraints, and variable conditions. In a standard linear programming model, the objective function is linear, meaning it is defined by a linear equation that aims to either maximize or minimize a particular quantity, such as profit or cost.

Additionally, the constraints of the model must also be linear, generally expressed as a set of linear inequalities or equations that restrict the values of the decision variables. These constraints represent the limitations or requirements that must be satisfied, such as resource usage or budget constraints.

Furthermore, it is essential for the variables in a linear programming model to be nonnegative, as negative values for quantities like production levels or resource amounts may not make practical sense in many real-world scenarios. This nonnegativity condition ensures that all decision variables represent feasible and realistic quantities.

Therefore, the formulation of a linear programming model typically includes a linear objective function, linear constraints, and nonnegative variables, making this the correct answer.